Unifying the integration, analysis and interpretation of multi-omic datasets: exploration of the disease networks of Obstructive Nephropathy in children

The wealth of data amassed by the utilization of various high-throughput techniques, in various layers of molecular dissection, stresses the critical role of the unification of the computational methodologies applied in biological data handling, storage, analysis and visualization. In this article, a generic workflow is showcased in a multi-omic dataset that is used to study Obstructive Nephropathy (ON) in children, integrating microarray data from several biological layers (transcriptomic, post-transcriptomic, proteomic). The workflow exploits raw measurements and through several analytical stages (preprocessing, statistical and functional), which entail various parsing steps, reaches the visualization stage of the heterogeneous, broader, molecular interacting network derived. This network, where the interconnected entities are exploiting the knowledge stored in public repositories, represents a systems level interpretation of the pathological state probed.

Statistical data mining of streaming motion data for fall detection in assistive environments

The analysis of human motion data is interesting for the purpose of activity recognition or emergency event detection, especially in the case of elderly or disabled people living independently in their homes. Several techniques have been proposed for identifying such distress situations using either motion, audio or video sensors on the monitored subject (wearable sensors) or the surrounding environment. The output of such sensors is data streams that require real time recognition, especially in emergency situations, thus traditional classification approaches may not be applicable for immediate alarm triggering or fall prevention. This paper presents a statistical mining methodology that may be used for the specific problem of real time fall detection. Visual data captured from the user's environment, using overhead cameras along with motion data are collected from accelerometers on the subject's body and are fed to the fall detection system. The paper includes the details of the stream data mining methodology incorporated in the system along with an initial evaluation of the achieved accuracy in detecting falls.

GOrevenge: a novel generic reverse engineering method for the identification of critical molecular players, through the use of ontologies

The ever-increasing use of ontologies in modern biological analysis and interpretation facilitates the understanding of the cellular procedures, their hierarchical organization, and their potential interactions at a system's level. Currently, the gene ontology serves as a paradigm, where through the annotation of whole genomes of certain organisms, genes subsets selected, either from high-throughput experiments or with an established pivotal role regarding the probed disease, can act as a starting point for the exploration of their underlying functional interconnections. This may also aid the elucidation of hidden regulatory mechanisms among genes. Reverse engineering the functional relevance of genes to specific cellular pathways and vice versa, through the exploitation of the inner structure of the ontological vocabularies, may help impart insight regarding the identification and prioritization of the critical role of specific genes. The proposed graph-theoretical method is showcased in a pancreatic cancer and a T-cell acute lymphoblastic leukemia gene set, incorporating edge and Resnik semantic similarity metrics, and systematically evaluated regarding its performance.

A medical ontology for intelligent web-based skin lesions image retrieval

Researchers have applied increasing efforts towards providing formal computational frameworks to consolidate the plethora of concepts and relations used in the medical domain. In the domain of skin related diseases, the variability of semantic features contained within digital skin images is a major barrier to the medical understanding of the symptoms and development of early skin cancers. The desideratum of making these standards machine-readable has led to their formalization in ontologies. In this work, in an attempt to enhance an existing Core Ontology for skin lesion images, hand-coded from image features, high quality images were analyzed by an autonomous ontology creation engine. We show that by exploiting agglomerative clustering methods with distance criteria upon the existing ontological structure, the original domain model could be enhanced with new instances, attributes and even relations, thus allowing for better classification and retrieval of skin lesion categories from the web.

Delineation and interpretation of gene networks towards their effect in cellular physiology- a reverse engineering approach for the identification of critical molecular players, through the use of ontologies

Exploiting ontologies, provides clues regarding the involvement of certain molecular processes in the cellular phenotypic manifestation. However, identifying individual molecular actors (genes, proteins, etc.) for targeted biological validation in a generic, prioritized, fashion, based in objective measures of their effects in the cellular physiology, remains a challenge. In this work, a new meta-analysis algorithm is proposed for the holistic interpretation of the information captured in -omic experiments, that is showcased in a transcriptomic, dynamic, DNA microarray dataset, which examines the effect of mastic oil treatment in Lewis lung carcinoma cells. Through the use of the Gene Ontology this algorithm relates genes to specific cellular pathways and vice versa in order to further reverse engineer the critical role of specific genes, starting from the results of various statistical enrichment analyses. The algorithm is able to discriminate candidate hub-genes, implying critical biochemical cross-talk. Moreover, performance measures of the algorithm are derived, when evaluated with respect to the differential expression gene list of the dataset.

An open data mining framework for the analysis of medical images: application on obstructive nephropathy microscopy images

This paper presents an open image-mining framework that provides access to tools and methods for the characterization of medical images. Several image processing and feature extraction operators have been implemented and exposed through Web Services. Rapid-Miner, an open source data mining system has been utilized for applying classification operators and creating the essential processing workflows. The proposed framework has been applied for the detection of salient objects in Obstructive Nephropathy microscopy images. Initial classification results are quite promising demonstrating the feasibility of automated characterization of kidney biopsy images.

GRISSOM platform: enabling distributed processing and management of biological data through fusion of grid and web technologies

Transcriptomic technologies have a critical impact in the revolutionary changes that reshape biological research. Through the recruitment of novel high-throughput instrumentation and advanced computational methodologies, an unprecedented wealth of quantitative data is produced. Microarray experiments are considered high-throughput, both in terms of data volumes (data intensive) and processing complexity (computationally intensive). In this paper, we present grids for in silico systems biology and medicine (GRISSOM), a web-based application that exploits GRID infrastructures for distributed data processing and management, of DNA microarrays (cDNA, Affymetrix, Illumina) through a generic, consistent, computational analysis framework. GRISSOM performs versatile annotation and integrative analysis tasks, through the use of third-party application programming interfaces, delivered as web services. In parallel, by conforming to service-oriented architectures, it can be encapsulated in other biomedical processing workflows, with the help of workflow enacting software, like Taverna Workbench, thus rendering access to its algorithms, transparent and generic. GRISSOM aims to set a generic paradigm of efficient metamining that promotes translational research in biomedicine, through the fusion of grid and semantic web computing technologies.

Classification of apoptosis using advanced clustering techniques on digital microscopic images

Programmed cell death, also known as apoptosis is of fundamental importance in many biological processes and also highly associated with serious diseases like cancer and HIV. The current paper presents an innovative method for apoptosis phenomenon characterization based on apoptotic cell quantification and detection using active contours. Subsequently, we employ appropriate data mining techniques and perform characterization of apoptosis on digital microscopic images. A particular class of clustering algorithms, utilizing information driven by the Principal Component Analysis, has been very successful in dealing with such data. In this work, we employ a recently proposed clustering algorithm to solve this real world clustering task.

Emergency fall incidents detection in assisted living environments utilizing motion, sound, and visual perceptual components

This paper presents the implementation details of a patient status awareness enabling human activity interpretation and emergency detection in cases, where the personal health is threatened like elder falls or patient collapses. The proposed system utilizes video, audio, and motion data captured from the patient's body using appropriate body sensors and the surrounding environment, using overhead cameras and microphone arrays. Appropriate tracking techniques are applied to the visual perceptual component enabling the trajectory tracking of persons, while proper audio data processing and sound directionality analysis in conjunction to motion information and subject's visual location can verify fall and indicate an emergency event. The postfall visual and motion behavior of the subject, which indicates the severity of the fall (e.g., if the person remains unconscious or patient recovers) is performed through a semantic representation of the patient's status, context and rules-based evaluation, and advanced classification. A number of advanced classification techniques have been examined in the framework of this study and their corresponding performance in terms of accuracy and efficiency in detecting an emergency situation has been thoroughly assessed.

Classification of dermatological images using advanced clustering techniques

Computer vision-based diagnosis systems have been widely used in dermatology, aiming at the early detection of skin cancer and more specifically the recognition of malignant melanoma tumor. This paper proposes a novel clustering technique for the characterization and categorization of pigmented skin lesions in dermatological images. Appropriate image processing techniques (i.e. segmentation, border detection, color and texture processing) are utilized for feature extraction. The proposed method uses Principal Component Analysis and is considered appropriate, since it is suitable for problems with high dimensional data. Initial experimental results have proved the superiority of this method against traditional ones.

Mobile healthcare information management utilizing Cloud Computing and Android OS

Cloud Computing provides functionality for managing information data in a distributed, ubiquitous and pervasive manner supporting several platforms, systems and applications. This work presents the implementation of a mobile system that enables electronic healthcare data storage, update and retrieval using Cloud Computing. The mobile application is developed using Google's Android operating system and provides management of patient health records and medical images (supporting DICOM format and JPEG2000 coding). The developed system has been evaluated using the Amazon's S3 cloud service. This article summarizes the implementation details and presents initial results of the system in practice.

Enabling distributed processing and management of biological data using the grid and web technologies

Transcriptomic technologies have dramatically reshaped modern biological research by deriving profiles of genome-wide expression of living organisms, and producing an unprecedented wealth of quantitative data. Given this characteristic, microarray experiments are considered high-throughput both in terms of data (data intensive) and processing (computationally intensive). GRISSOM Web Tools enable the exploitation of GRID resources for DNA microarray data distributed processing and management. It provides experts with a complete web-based solution for managing, searching and disseminating biological knowledge in the context of gene expression patterns on a genomic scale. Through the incorporation of novel workflows and various web services, the platform is gradually transformed to a powerful environment for knowledge discovery in the biomedical research.

KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database

BACKGROUND

The KEGG Pathway database is a valuable collection of metabolic pathway maps. Nevertheless, the production of simulation capable metabolic networks from KEGG Pathway data is a challenging complicated work, regardless the already developed tools for this scope. Originally used for illustration purposes, KEGG Pathways through KGML (KEGG Markup Language) files, can provide complete reaction sets and introduce species versioning, which offers advantages for the scope of cellular metabolism simulation modelling. In this project, KEGGconverter is described, implemented also as a web-based application, which uses as source KGML files, in order to construct integrated pathway SBML models fully functional for simulation purposes.

RESULTS

A case study of the integration of six human metabolic pathways from KEGG depicts the ability of KEGGconverter to automatically produce merged and converted to SBML fully functional pathway models, enhanced with default kinetics. The suitability of the developed tool is demonstrated through a comparison with other state-of-the art relevant software tools for the same data fusion and conversion tasks, thus illustrating the problems and the relevant workflows. Moreover, KEGGconverter permits the inclusion of additional reactions in the resulting model which represent flux cross-talk with neighbouring pathways, providing in this way improved simulative accuracy. These additional reactions are introduced by exploiting relevant semantic information for the elements of the KEGG Pathways database. The architecture and functionalities of the web-based application are presented.

CONCLUSION

KEGGconverter is capable of producing integrated analogues of metabolic pathways appropriate for simulation tasks, by inputting only KGML files. The web application acts as a user friendly shell which transparently enables the automated biochemically correct pathway merging, conversion to SBML format, proper renaming of the species, and insertion of default kinetic properties for the pertaining reactions. The tool is available at: http://www.grissom.gr/keggconverter.

Support Vectors Machine-based identification of heart valve diseases using heart sounds

Taking into account that heart auscultation remains the dominant method for heart examination in the small health centers of the rural areas and generally in primary healthcare set-ups, the enhancement of this technique would aid significantly in the diagnosis of heart diseases. In this context, the present paper initially surveys the research that has been conducted concerning the exploitation of heart sound signals for automated and semi-automated detection of pathological heart conditions. Then it proposes an automated diagnosis system for the identification of heart valve diseases based on the Support Vector Machines (SVM) classification of heart sounds. This system performs a highly difficult diagnostic task (even for experienced physicians), much more difficult than the basic diagnosis of the existence or not of a heart valve disease (i.e. the classification of a heart sound as 'healthy' or 'having a heart valve disease'): it identifies the particular heart valve disease. The system was applied in a representative global dataset of 198 heart sound signals, which come both from healthy medical cases and from cases suffering from the four most usual heart valve diseases: aortic stenosis (AS), aortic regurgitation (AR), mitral stenosis (MS) and mitral regurgitation (MR). Initially the heart sounds were successfully categorized using a SVM classifier as normal or disease-related and then the corresponding murmurs in the unhealthy cases were classified as systolic or diastolic. For the heart sounds diagnosed as having systolic murmur we used a SVM classifier for performing a more detailed classification of them as having aortic stenosis or mitral regurgitation. Similarly for the heart sounds diagnosed as having diastolic murmur we used a SVM classifier for classifying them as having aortic regurgitation or mitral stenosis. Alternative classifiers have been applied to the same data for comparison (i.e. back-propagation neural networks, k-nearest-neighbour and naïve Bayes classifiers), however their performance for the same diagnostic problems was lower than the SVM classifiers proposed in this work.

Computer-supported angiogenesis quantification using image analysis and statistical averaging

Angiogenesis is a complex process, involving multiple crosstalks among tumor, endothelial, and stromal cells in order to establish a biochemical network for oxygen and nutrients supply, necessary for the promotion of tumor growth. In this sense, measuring angiogenic activity is considered an informative marker of tumor growth or its inhibition. One of the most popular testbeds for the study of angiogenesis is developing chick embryo and its chorioallantoic membrane (CAM). In this paper, an automated image analysis and statistical processing method for the extraction of features informative for the angiogenic process is proposed and a Web-based tool that provides an unbiased quantification of the microvessel density and growth in angiogenic CAM images is described. The applicability of the tool is tested in two datasets, concerning: 1) the quantification and subsequent detection of tumor growth at different stages of embryonic development and 2) the inhibitory effect of dexamethasone (i.e., an inhibitor of the angiogenesis phenomenon) over a series of CAM samples. Experimental results presented in this paper indicate the efficiency of the automated angiogenesis quantification method regarding both tumor growth and inhibition detection.

Automated angiogenesis quantification through advanced image processing techniques

Angiogenesis, the formation of blood vessels in tumors, is an interactive process between tumor, endothelial and stromal cells in order to create a network for oxygen and nutrients supply, necessary for tumor growth. According to this, angiogenic activity is considered a suitable method for both tumor growth or inhibition detection. The angiogenic potential is usually estimated by counting the number of blood vessels in particular sections. One of the most popular assay tissues to study the angiogenesis phenomenon is the developing chick embryo and its chorioallantoic membrane (CAM), which is a highly vascular structure lining the inner surface of the egg shell. The aim of this study was to develop and validate an automated image analysis method that would give an unbiased quantification of the micro-vessel density and growth in angiogenic CAM images. The presented method has been validated by comparing automated results to manual counts over a series of digital chick embryo photos. The results indicate the high accuracy of the tool, which has been thus extensively used for tumor growth detection at different stages of embryonic development.

Modeling risk in distributed healthcare information systems

This paper presents a modeling approach for performing a risk analysis study of networked healthcare information systems. The proposed method is based on CRAMM for studying the assets, threats and vulnerabilities of the distributed information system, and models their interrelationships using Bayesian networks. The most critical events are identified and prioritized, based on "what - if" studies of system operation. The proposed risk analysis framework has been applied to a healthcare information network operating in the North Aegean Region in Greece.

Automated cell apoptosis characterization using active contours

Programmed cell death, also known as apoptosis, is of fundamental importance in many biological processes and also highly associated with serious diseases like cancer and HIV. The current paper presents an innovative method for apoptosis phenomenon characterization based on apoptotic cell quantification and detection using active contours (snakes). Evaluation results against manual counts have proved the high accuracy and efficiency of the developed method.

Advanced medical video services through context-aware medical networks

The aim of this paper is to present a framework for advanced medical video delivery services, through network and patient-state awareness. Under this scope a context-aware medical networking platform is described. The developed platform enables proper medical video data coding and transmission according to both a) network availability and/or quality and b) patient status, optimizing thus network performance and telediagnosis. An evaluation platform has been developed based on scalable H.264 coding of medical videos. Corresponding results of video transmission over a WiMax network have proved the effectiveness and efficiency of the platform providing proper video content delivery.

EmerLoc: Location-based services for emergency medical incidents

BACKGROUND

Recent developments in positioning systems and telecommunications have provided the technology needed for the development of location aware medical applications. We developed a system, named EmerLoc, which is based upon this technology and uses a set of sensors that are attached to the patient's body, a micro-computing unit which is responsible for processing the sensor readings and a central monitoring unit, which coordinates the data flow.

OBJECTIVE

To demonstrate that the proposed system is technically feasible and acceptable for the potential users.

METHOD

Transmission speed is assessed mostly by means of transmission of DICOM compliant images in various operational scenarios. The positioning functionality was established both outdoor using GPS and indoor using the UCLA Nibble system. User acceptability was assessed in a hospital setting by 15 physicians who filled in a questionnaire after having used the system in an experimental setting.

RESULTS

Transmission speeds ranged from 88kB/s for a IEEE 802.11 infrastructure to 2.5kB/s for a GSM/GPRS scenario. Positioning accuracy based on GPS was 5-10m. The physicians rated the technical aspects on average above 3 on a 5-point scale. Only the data presentation was assessed to be not satisfactory (2.81 on a 5-point scale).

CONCLUSION

The reported results prove the feasibility of the proposed architecture and its alignment with widely established practices and standards, while the reaction of potential users who evaluated the system is quite positive.

Pervasive E-health services using the DVB-RCS communication technology

Two-way satellite broadband communication technologies, such as the Digital Video Broadcasting with Return Channel via Satellite (DVB-RCS) technology, endeavour to offer attractive wide-area broadband connectivity for telemedicine applications, taking into consideration the available data rates, Quality of Service (QoS) provision, survivability, flexibility and operational costs, even in remote areas and isolated regions where the terrestrial technologies suffer. This paper describes a wide-area tele-medicine platform, specially suited for homecare services, based on the DVB-RCS and Wi-Fi communication technologies. The presented platform combines medical data acquisition and transfer, patient remote monitoring and teleconference services. Possible operational scenarios concerning this platform and experimental results regarding tele-monitoring, videoconference and medical data transfer are also provided and discussed in the paper.

Medical Image Compression using Wavelet Transform on Mobile Devices with ROI coding support

Medical applications have already been integrated into mobile devices (e.g. Tablet PC's and PDA's) and are being used by medical personnel in treatment centers, for retrieving and examining patient data and medical images. Network transmission and image data processing are key issues in such platforms, due to the significant image file sizes. Wavelet transform has been considered to be a highly efficient technique of image compression resulting in both lossless and lossy compressed images of great accuracy, enabling its use on medical images. This paper discusses a Picture Archiving and Communication Systems (PACS) application designed for viewing DICOM compliant medical images using Wavelet compression with ROI coding support, on mobile devices. In addition, it presents initial results from its pilot application and demonstrates its performance over heterogeneous radio network segments, like IEEE 802.11b, GPRS and DVB-H.

Enabling collaborative medical diagnosis over the Internet via peer-to-peer distribution of electronic health records

Recent developments in networking and computing technologies and the expansion of the electronic health record system have enabled the possibility of online collaboration between geographically distributed medical personnel. In this context, the paper presents a Web-based application, which implements a collaborative working environment for physicians by enabling the peer-to-peer exchange of electronic health records. The paper treats technological issues such as Video, Audio and Message Communication, Workspace Management, Distributed Medical Data Management and exchange, while it emphasizes on the Security issues arisen, due to the sensitive and private nature of the medical information. In the paper, we present initial results from the system in practice and measurements regarding transmission times and bandwidth requirements. A wavelet based image compression scheme is also introduced for reducing network delays. A number of physicians were asked to use the platform for testing purposes and for measuring user acceptance. The system was considered by them to be very useful, as they found that the platform simulated very well the personal contact between them and their colleagues during medical meetings.

Neural network-based diagnostic and prognostic estimations in breast cancer microscopic instances

This paper deals with breast cancer diagnostic and prognostic estimations employing neural networks over the Wisconsin Breast Cancer datasets, which consist of measurements taken from breast cancer microscopic instances. A probabilistic approach is dedicated to solve the diagnosis problem, detecting malignancy among instances derived from the Fine Needle Aspirate test, while regression algorithms estimate the time interval that possibly correspond to the right end-point of the patients' disease-free survival time or the time where the tumour recurs (time-to-recur). For the diagnosis problem, the accuracy of the neural network in terms of sensitivity and specificity was measured at 98.6 and 97.5% respectively, using the leave-one-out test method. As far as the prognosis problem is concerned, the accuracy of the neural network was measured through a stratified tenfold cross-validation approach. Sensitivity ranged between 80.5 and 91.8%, while specificity ranged between 91.9 and 97.9%, depending on the tested fold and the partition of the predicted period. The prognostic recurrence predictions were then further evaluated using survival analysis and compared with other techniques found in literature.

Overview: Computational analysis and decision support systems in oncology

Computational analysis tools and decision support systems have increased their penetration in the support of clinical processes and management of medical data and knowledge. Applications range from adjunct tools for diagnosis and disease investigation to the treatment and monitoring of therapeutic procedures. As all medical fields, the field of oncology is affected. This special issue includes studies presenting research and applications of computational intelligence in oncology, covering four main areas: i) decision support systems (DSS) and artificial intelligence (AI) applications in oncology; ii) design and assessment of classification tools in oncology; iii) intelligent accessing, retrieving, and storing of medical images; and iv) intelligent telemedicine and telehealth applications in oncology.

Computational vision systems for the detection of malignant melanoma

In recent years, computational vision-based diagnostic systems for dermatology have demonstrated significant progress. We review these systems by first presenting the installation, visual features utilized for skin lesion classification and the methods for defining them. We also describe how to extract these features through digital image processing methods, i.e. segmentation, registration, border detection, color and texture processing, and present how to use the extracted features for skin lesion classification by employing artificial intelligence methods, i.e. discriminant analysis, neural networks, and support vector machines. Finally, we compare these techniques in discriminating malignant melanoma tumors versus dysplastic naevi lesions.

An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images

This paper describes an integrated prototype computer-based system for the characterization of skin digital images. The first stage includes an image acquisition arrangement designed for capturing skin images, under reproducible conditions. The system processes the captured images and performs unsupervised image segmentation and image registration utilizing an efficient algorithm based on the log-polar transform of the images' Fourier spectrum. Border- and color-based features, extracted from the digital images of skin lesions, were used to construct a classification module for the recognition of malignant melanoma versus dysplastic nevus. Different methods, drawn from the fields of artificial intelligence (neural networks) and statistical modeling (discriminant analysis), were used in order to find the best classification rules and to compare the results of different approaches to the problem.

PKI-based secure mobile access to electronic health services and data

Recent research works examine the potential employment of public-key cryptography schemes in e-health environments. In such systems, where a Public Key Infrastructure (PKI) is established beforehand, Attribute Certificates (ACs) and public key enabled protocols like TLS, can provide the appropriate mechanisms to effectively support authentication, authorization and confidentiality services. In other words, mutual trust and secure communications between all the stakeholders, namely physicians, patients and e-health service providers, can be successfully established and maintained. Furthermore, as the recently introduced mobile devices with access to computer-based patient record systems are expanding, the need of physicians and nurses to interact increasingly with such systems arises. Considering public key infrastructure requirements for mobile online health networks, this paper discusses the potential use of Attribute Certificates (ACs) in an anticipated trust model. Typical trust interactions among doctors, patients and e-health providers are presented, indicating that resourceful security mechanisms and trust control can be obtained and implemented. The application of attribute certificates to support medical mobile service provision along with the utilization of the de-facto TLS protocol to offer competent confidentiality and authorization services is also presented and evaluated through experimentation, using both the 802.11 WLAN and General Packet Radio Service (GPRS) networks.

Design and Implementation of a Calibrated Store and Forward Imaging System for Teledermatology

The paper presents a computer-based imaging system aiming to support telemedicine examination sessions in dermatology. Many studies have proved the inadequacy of general practitioners to diagnose successfully common dermatological diseases; some of them may prove fatal if not diagnosed at their early stages (e.g., melanoma). Thus the need for telemedicine systems customized for dermatology becomes obvious for distant rural areas, where dermatological care is usually provided by general doctors. We treat technological issues such as image acquisition, camera calibration, illumination, data transmission, and data compression, and propose a store and forward architecture for image transmission. We also include a study of the effect that image compression quality factor has in the diagnostic value of the skin digital images, along with some initial results and conclusions from the pilot use of the system.

A system for the acquisition of reproducible digital skin lesions images

A major issue concerning the design and implementation of an image acquisition system for skin lesions is its ability to capture reproducible images. The reproducibility is considered essential for image analysis and for the comparison of sequential images during follow-up studies. This paper describes a prototype image acquisition system that includes a standardized illumination and capturing geometry with polarizing filters and a series of software corrections: Calibration to Black, White and Color for color constancy, Internal camera Parameters adjustment and Pose extraction for stereo vision, Shading correction and Noise Filtering for color quality. The validity of the calibration procedure and the images' reproducibility were tested by capturing sample images in three different lighting conditions: dark, medium and intense lighting. For each case the average values of the three color planes RGB and their standard deviations were calculated; the measured error differences ranged between 0.7 and 12.9 (in the 0-255 scale). Preliminary experiments for stereo measurements provided repeatability of about 0.3 mm. The above results demonstrate the reproducibility of the captured images at a satisfactory level. The developed prototype was also evaluated clinically, for its ability to support the construction of knowledge-based decision systems and for telemedicine, thus to support telemedical sessions in dermatology.